Overview

Dataset statistics

Number of variables19
Number of observations89
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 KiB
Average record size in memory148.7 B

Variable types

NUM18
CAT1

Reproduction

Analysis started2020-07-25 00:03:37.625901
Analysis finished2020-07-25 00:05:59.915891
Duration2 minutes and 22.29 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

ELECTORADO is highly correlated with JUNTAS and 14 other fieldsHigh correlation
JUNTAS is highly correlated with ELECTORADO and 14 other fieldsHigh correlation
TOTAL GENERAL is highly correlated with JUNTAS and 14 other fieldsHigh correlation
TOTAL is highly correlated with JUNTAS and 14 other fieldsHigh correlation
UNIDAD SOCIAL CRISTIANA is highly correlated with JUNTAS and 14 other fieldsHigh correlation
INTEGRACIÓN NACIONAL is highly correlated with JUNTAS and 14 other fieldsHigh correlation
ALIANZA PATRIÓTICA is highly correlated with JUNTAS and 14 other fieldsHigh correlation
RENOVACIÓN COSTARRICENSE is highly correlated with JUNTAS and 14 other fieldsHigh correlation
FRENTE AMPLIO is highly correlated with JUNTAS and 14 other fieldsHigh correlation
LIBERACIÓN NACIONAL is highly correlated with JUNTAS and 14 other fieldsHigh correlation
MOVIMIENTO LIBERTARIO is highly correlated with JUNTAS and 14 other fieldsHigh correlation
ACCIÓN CIUDADANA is highly correlated with JUNTAS and 14 other fieldsHigh correlation
ACCESIBILIDAD SIN EXCLUSIÓN is highly correlated with JUNTAS and 14 other fieldsHigh correlation
NULOS is highly correlated with JUNTAS and 14 other fieldsHigh correlation
BLANCOS is highly correlated with JUNTAS and 14 other fieldsHigh correlation
ABSOLUTO is highly correlated with JUNTAS and 14 other fieldsHigh correlation
% is highly correlated with % PARTICIPACIÓNHigh correlation
% PARTICIPACIÓN is highly correlated with %High correlation
PROVINCIA Y CANTÓN has unique values Unique
ELECTORADO has unique values Unique
TOTAL GENERAL has unique values Unique
% PARTICIPACIÓN has unique values Unique
TOTAL has unique values Unique
LIBERACIÓN NACIONAL has unique values Unique
ACCIÓN CIUDADANA has unique values Unique
ABSOLUTO has unique values Unique
% has unique values Unique

Variables

PROVINCIA Y CANTÓN
Categorical

UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
SARAPIQUÍ
 
1
MATINA
 
1
ALVARADO
 
1
HOJANCHA
 
1
OROTINA
 
1
Other values (84)
84
ValueCountFrequency (%) 
SARAPIQUÍ11.1%
 
MATINA11.1%
 
ALVARADO11.1%
 
HOJANCHA11.1%
 
OROTINA11.1%
 
SAN RAMÓN11.1%
 
ASERRÍ11.1%
 
PUNTARENAS11.1%
 
SANTA ANA11.1%
 
GUATUSO11.1%
 
Other values (79)7988.8%
 
2020-07-24T18:06:00.187360image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length21
Median length8
Mean length9.011235955
Min length3

JUNTAS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count69
Unique (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.04494382022472
Minimum11
Maximum6617
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:00.391860image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20.4
Q137
median64
Q3119
95-th percentile662.8
Maximum6617
Range6606
Interquartile range (IQR)82

Descriptive statistics

Standard deviation745.8598746
Coefficient of variation (CV)3.343989161
Kurtosis63.29110087
Mean223.0449438
Median Absolute Deviation (MAD)33
Skewness7.58219932
Sum19851
Variance556306.9525
2020-07-24T18:06:00.584884image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2855.6%
 
4433.4%
 
8422.2%
 
6422.2%
 
3122.2%
 
7222.2%
 
2922.2%
 
9122.2%
 
6622.2%
 
2522.2%
 
Other values (59)6573.0%
 
ValueCountFrequency (%) 
1111.1%
 
1411.1%
 
1611.1%
 
1811.1%
 
2011.1%
 
ValueCountFrequency (%) 
661711.1%
 
223211.1%
 
125211.1%
 
74611.1%
 
70211.1%
 

ELECTORADO
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95140.14606741573
Minimum3798
Maximum2822491
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:00.792915image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3798
5-th percentile7571.6
Q113404
median26649
Q351433
95-th percentile270889.6
Maximum2822491
Range2818693
Interquartile range (IQR)38029

Descriptive statistics

Standard deviation319865.0736
Coefficient of variation (CV)3.362041019
Kurtosis62.05374757
Mean95140.14607
Median Absolute Deviation (MAD)15168
Skewness7.50483467
Sum8467473
Variance1.023136653e+11
2020-07-24T18:06:01.376846image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1779111.1%
 
2118311.1%
 
1262311.1%
 
902511.1%
 
3695211.1%
 
3690011.1%
 
25940811.1%
 
2575311.1%
 
900411.1%
 
1706911.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
379811.1%
 
396311.1%
 
478911.1%
 
493511.1%
 
739011.1%
 
ValueCountFrequency (%) 
282249111.1%
 
100817711.1%
 
52421511.1%
 
32878211.1%
 
27854411.1%
 

TOTAL GENERAL
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65758.88764044947
Minimum2669.0
Maximum1950847.000000001
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:01.591865image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2669
5-th percentile5194.2
Q19529
median17626
Q336209
95-th percentile184039
Maximum1950847
Range1948178
Interquartile range (IQR)26680

Descriptive statistics

Standard deviation221537.2018
Coefficient of variation (CV)3.36893171
Kurtosis61.56928896
Mean65758.88764
Median Absolute Deviation (MAD)11165
Skewness7.472997477
Sum5852541
Variance4.907873176e+10
2020-07-24T18:06:01.799533image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2030811.1%
 
2899711.1%
 
2917411.1%
 
952911.1%
 
1027111.1%
 
13772111.1%
 
2538511.1%
 
266911.1%
 
646111.1%
 
7984011.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
266911.1%
 
298011.1%
 
321511.1%
 
370311.1%
 
509511.1%
 
ValueCountFrequency (%) 
195084711.1%
 
70428411.1%
 
37495211.1%
 
24370511.1%
 
20370511.1%
 

% PARTICIPACIÓN
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.91530489967901
Minimum54.227168865629835
Maximum82.36868944416115
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:02.029149image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum54.22716887
5-th percentile58.34710967
Q164.51171447
median70.09112923
Q373.53369654
95-th percentile76.34258792
Maximum82.36868944
Range28.14152058
Interquartile range (IQR)9.021982068

Descriptive statistics

Standard deviation6.026328075
Coefficient of variation (CV)0.08744542426
Kurtosis-0.4479242398
Mean68.9153049
Median Absolute Deviation (MAD)4.032454177
Skewness-0.5321358099
Sum6133.462136
Variance36.31663006
2020-07-24T18:06:02.214794image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
64.5131771611.1%
 
71.6982763111.1%
 
70.2738283311.1%
 
74.8966047911.1%
 
67.1330131611.1%
 
74.8143390511.1%
 
62.6340148811.1%
 
75.5095989911.1%
 
67.8383555911.1%
 
57.7770513211.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
54.2271688711.1%
 
54.2810256411.1%
 
56.6613598411.1%
 
57.7770513211.1%
 
58.2310780911.1%
 
ValueCountFrequency (%) 
82.3686894411.1%
 
77.3485186811.1%
 
76.6263801911.1%
 
76.572958511.1%
 
76.3551509111.1%
 

TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64426.95505617977
Minimum2597
Maximum1911333
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:02.865251image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2597
5-th percentile5058.8
Q19298
median17319
Q335464
95-th percentile180702.6
Maximum1911333
Range1908736
Interquartile range (IQR)26166

Descriptive statistics

Standard deviation217152.3419
Coefficient of variation (CV)3.370520022
Kurtosis61.4626494
Mean64426.95506
Median Absolute Deviation (MAD)10949
Skewness7.466281048
Sum5733999
Variance4.715513961e+10
2020-07-24T18:06:03.099809image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
678211.1%
 
4563011.1%
 
527911.1%
 
1102811.1%
 
7849811.1%
 
1193911.1%
 
191133311.1%
 
1011611.1%
 
1731911.1%
 
1389511.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
259711.1%
 
292111.1%
 
316311.1%
 
360611.1%
 
497611.1%
 
ValueCountFrequency (%) 
191133311.1%
 
69276511.1%
 
36784311.1%
 
23914611.1%
 
20049311.1%
 

UNIDAD SOCIAL CRISTIANA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count88
Unique (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2498.224719101124
Minimum99.0
Maximum74113.99999999999
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:03.338107image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile216.4
Q1366
median661
Q31466
95-th percentile7336.6
Maximum74114
Range74015
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation8339.726553
Coefficient of variation (CV)3.338261162
Kurtosis63.68603094
Mean2498.224719
Median Absolute Deviation (MAD)328
Skewness7.601494575
Sum222342
Variance69551038.97
2020-07-24T18:06:03.572274image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
45922.2%
 
7411411.1%
 
36311.1%
 
46611.1%
 
36611.1%
 
51011.1%
 
179211.1%
 
22611.1%
 
35911.1%
 
52211.1%
 
Other values (78)7887.6%
 
ValueCountFrequency (%) 
9911.1%
 
13611.1%
 
14211.1%
 
19011.1%
 
21011.1%
 
ValueCountFrequency (%) 
7411411.1%
 
2460911.1%
 
1188711.1%
 
1014311.1%
 
777711.1%
 

INTEGRACIÓN NACIONAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count47
Unique (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.0674157303371
Minimum2.0
Maximum2049.000000000001
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:03.820329image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q17
median15
Q339
95-th percentile192.8
Maximum2049
Range2047
Interquartile range (IQR)32

Descriptive statistics

Standard deviation240.3277023
Coefficient of variation (CV)3.479610461
Kurtosis55.35512631
Mean69.06741573
Median Absolute Deviation (MAD)10
Skewness7.118684271
Sum6147
Variance57757.40449
2020-07-24T18:06:04.307080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
555.6%
 
655.6%
 
1455.6%
 
455.6%
 
1144.5%
 
844.5%
 
344.5%
 
1733.4%
 
2033.4%
 
1033.4%
 
Other values (37)4853.9%
 
ValueCountFrequency (%) 
222.2%
 
344.5%
 
455.6%
 
555.6%
 
655.6%
 
ValueCountFrequency (%) 
204911.1%
 
97011.1%
 
27511.1%
 
24011.1%
 
20211.1%
 

ALIANZA PATRIÓTICA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count59
Unique (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.4494382022472
Minimum5.0
Maximum3158.0000000000005
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:04.508843image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.8
Q119
median34
Q364
95-th percentile329
Maximum3158
Range3153
Interquartile range (IQR)45

Descriptive statistics

Standard deviation351.9448889
Coefficient of variation (CV)3.306216499
Kurtosis66.00859075
Mean106.4494382
Median Absolute Deviation (MAD)20
Skewness7.746596217
Sum9474
Variance123865.2048
2020-07-24T18:06:04.715944image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1366.7%
 
1055.6%
 
2233.4%
 
833.4%
 
2033.4%
 
2133.4%
 
2733.4%
 
4733.4%
 
3633.4%
 
3422.2%
 
Other values (49)5561.8%
 
ValueCountFrequency (%) 
522.2%
 
833.4%
 
1055.6%
 
1111.1%
 
1211.1%
 
ValueCountFrequency (%) 
315811.1%
 
87011.1%
 
65911.1%
 
38811.1%
 
34511.1%
 

RENOVACIÓN COSTARRICENSE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count84
Unique (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.0561797752809
Minimum8
Maximum13945
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:04.925815image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile18.6
Q169
median115
Q3258
95-th percentile1371.6
Maximum13945
Range13937
Interquartile range (IQR)189

Descriptive statistics

Standard deviation1562.111991
Coefficient of variation (CV)3.323245301
Kurtosis64.62227322
Mean470.0561798
Median Absolute Deviation (MAD)76
Skewness7.639114962
Sum41835
Variance2440193.872
2020-07-24T18:06:05.149214image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
22222.2%
 
1422.2%
 
5422.2%
 
9122.2%
 
6922.2%
 
3211.1%
 
28911.1%
 
16211.1%
 
10211.1%
 
3811.1%
 
Other values (74)7483.1%
 
ValueCountFrequency (%) 
811.1%
 
1311.1%
 
1422.2%
 
1711.1%
 
2111.1%
 
ValueCountFrequency (%) 
1394511.1%
 
385211.1%
 
278811.1%
 
252311.1%
 
151011.1%
 

FRENTE AMPLIO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count74
Unique (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.60674157303384
Minimum3.0
Maximum6782.000000000005
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:05.761712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.4
Q122
median61
Q3128
95-th percentile770.8
Maximum6782
Range6779
Interquartile range (IQR)106

Descriptive statistics

Standard deviation771.9292628
Coefficient of variation (CV)3.376668848
Kurtosis61.0876166
Mean228.6067416
Median Absolute Deviation (MAD)45
Skewness7.439231152
Sum20346
Variance595874.7868
2020-07-24T18:06:05.975662image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
933.4%
 
5133.4%
 
2622.2%
 
3722.2%
 
522.2%
 
3422.2%
 
1622.2%
 
6322.2%
 
4022.2%
 
622.2%
 
Other values (64)6775.3%
 
ValueCountFrequency (%) 
311.1%
 
522.2%
 
622.2%
 
711.1%
 
811.1%
 
ValueCountFrequency (%) 
678211.1%
 
253611.1%
 
93711.1%
 
90511.1%
 
83011.1%
 

LIBERACIÓN NACIONAL
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30219.640449438222
Minimum1307.0
Maximum896516.0000000009
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:06.184791image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1307
5-th percentile2382.8
Q14569
median8264
Q315373
95-th percentile85455
Maximum896516
Range895209
Interquartile range (IQR)10804

Descriptive statistics

Standard deviation101763.8338
Coefficient of variation (CV)3.36747335
Kurtosis61.66868473
Mean30219.64045
Median Absolute Deviation (MAD)4940
Skewness7.479021383
Sum2689548
Variance1.035587788e+10
2020-07-24T18:06:06.401000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1651911.1%
 
480911.1%
 
1340311.1%
 
1303311.1%
 
1841511.1%
 
4029811.1%
 
462611.1%
 
456911.1%
 
303511.1%
 
261711.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
130711.1%
 
136111.1%
 
164211.1%
 
211811.1%
 
225011.1%
 
ValueCountFrequency (%) 
89651611.1%
 
32297611.1%
 
16665511.1%
 
11892611.1%
 
9347111.1%
 

MOVIMIENTO LIBERTARIO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count88
Unique (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13475.999999999995
Minimum355.0
Maximum399787.9999999998
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:06.627503image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum355
5-th percentile806.6
Q12049
median3743
Q36801
95-th percentile41071
Maximum399788
Range399433
Interquartile range (IQR)4752

Descriptive statistics

Standard deviation44859.71167
Coefficient of variation (CV)3.328859577
Kurtosis64.29085849
Mean13476
Median Absolute Deviation (MAD)1968
Skewness7.63085781
Sum1199364
Variance2012393731
2020-07-24T18:06:07.262931image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
184922.2%
 
4641411.1%
 
680111.1%
 
366411.1%
 
241011.1%
 
124811.1%
 
4007811.1%
 
8059011.1%
 
1640011.1%
 
12211611.1%
 
Other values (78)7887.6%
 
ValueCountFrequency (%) 
35511.1%
 
54911.1%
 
68411.1%
 
71911.1%
 
73111.1%
 
ValueCountFrequency (%) 
39978811.1%
 
12211611.1%
 
8059011.1%
 
4641411.1%
 
4173311.1%
 

ACCIÓN CIUDADANA
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16141.921348314609
Minimum339.0
Maximum478877.0000000001
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:07.496487image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum339
5-th percentile568.8
Q11737
median3733
Q39432
95-th percentile49607.8
Maximum478877
Range478538
Interquartile range (IQR)7695

Descriptive statistics

Standard deviation55442.03843
Coefficient of variation (CV)3.434661663
Kurtosis57.4787308
Mean16141.92135
Median Absolute Deviation (MAD)2520
Skewness7.221096791
Sum1436631
Variance3073819625
2020-07-24T18:06:07.714567image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2388611.1%
 
1133411.1%
 
80711.1%
 
55611.1%
 
1146711.1%
 
58811.1%
 
221611.1%
 
129011.1%
 
306811.1%
 
46311.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
33911.1%
 
43811.1%
 
46311.1%
 
53311.1%
 
55611.1%
 
ValueCountFrequency (%) 
47887711.1%
 
20038211.1%
 
9739911.1%
 
5609511.1%
 
5542511.1%
 

ACCESIBILIDAD SIN EXCLUSIÓN
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count85
Unique (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1216.9887640449435
Minimum22.0
Maximum36103.999999999985
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:07.946098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile30.8
Q1135
median271
Q3732
95-th percentile3483.8
Maximum36104
Range36082
Interquartile range (IQR)597

Descriptive statistics

Standard deviation4156.140981
Coefficient of variation (CV)3.41510218
Kurtosis58.5977558
Mean1216.988764
Median Absolute Deviation (MAD)202
Skewness7.281414298
Sum108312
Variance17273507.85
2020-07-24T18:06:08.200807image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3022.2%
 
24822.2%
 
14022.2%
 
13522.2%
 
5111.1%
 
29011.1%
 
27511.1%
 
13311.1%
 
73411.1%
 
6911.1%
 
Other values (75)7584.3%
 
ValueCountFrequency (%) 
2211.1%
 
2411.1%
 
2611.1%
 
3022.2%
 
3211.1%
 
ValueCountFrequency (%) 
3610411.1%
 
1445411.1%
 
676011.1%
 
502611.1%
 
367711.1%
 

NULOS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count86
Unique (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1097.3595505617975
Minimum33.0
Maximum32554.999999999993
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:08.794399image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile72.8
Q1173
median312
Q3615
95-th percentile3685
Maximum32555
Range32522
Interquartile range (IQR)442

Descriptive statistics

Standard deviation3637.349488
Coefficient of variation (CV)3.314637838
Kurtosis65.3812119
Mean1097.359551
Median Absolute Deviation (MAD)176
Skewness7.704404286
Sum97665
Variance13230311.3
2020-07-24T18:06:09.018348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
28722.2%
 
20322.2%
 
12822.2%
 
306311.1%
 
32311.1%
 
18511.1%
 
17311.1%
 
12411.1%
 
97911.1%
 
6611.1%
 
Other values (76)7685.4%
 
ValueCountFrequency (%) 
3311.1%
 
5011.1%
 
5611.1%
 
6611.1%
 
6811.1%
 
ValueCountFrequency (%) 
3255511.1%
 
967411.1%
 
579811.1%
 
404511.1%
 
376311.1%
 

BLANCOS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count71
Unique (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.57303370786516
Minimum9.0
Maximum6959.0
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:09.227490image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile22
Q143
median71
Q3120
95-th percentile801.4
Maximum6959
Range6950
Interquartile range (IQR)77

Descriptive statistics

Standard deviation772.6696326
Coefficient of variation (CV)3.293940571
Kurtosis66.97621382
Mean234.5730337
Median Absolute Deviation (MAD)37
Skewness7.809906222
Sum20877
Variance597018.3611
2020-07-24T18:06:09.450370image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2944.5%
 
7133.4%
 
2233.4%
 
9222.2%
 
4322.2%
 
8622.2%
 
5522.2%
 
4822.2%
 
6022.2%
 
3122.2%
 
Other values (61)6573.0%
 
ValueCountFrequency (%) 
911.1%
 
1611.1%
 
1911.1%
 
2233.4%
 
2411.1%
 
ValueCountFrequency (%) 
695911.1%
 
184511.1%
 
131111.1%
 
88211.1%
 
80511.1%
 

ABSOLUTO
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29381.25842696629
Minimum983.0
Maximum871644.0000000001
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:09.715991image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum983
5-th percentile1763.2
Q13878
median7652
Q315117
95-th percentile85875
Maximum871644
Range870661
Interquartile range (IQR)11239

Descriptive statistics

Standard deviation98484.38919
Coefficient of variation (CV)3.351945916
Kurtosis62.74764024
Mean29381.25843
Median Absolute Deviation (MAD)4290
Skewness7.546378403
Sum2614932
Variance9699174913
2020-07-24T18:06:10.255868image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8640711.1%
 
1112511.1%
 
1242011.1%
 
2594211.1%
 
643611.1%
 
1116111.1%
 
8507711.1%
 
663411.1%
 
412511.1%
 
423011.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
98311.1%
 
112911.1%
 
123211.1%
 
138311.1%
 
157411.1%
 
ValueCountFrequency (%) 
87164411.1%
 
30389311.1%
 
14926311.1%
 
10486811.1%
 
8640711.1%
 

%
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count89
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.084695100320985
Minimum17.63131055583886
Maximum45.772831134370165
Zeros0
Zeros (%)0.0%
Memory size712.0 B
2020-07-24T18:06:10.458912image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum17.63131056
5-th percentile23.65741208
Q126.46630346
median29.90887077
Q335.48828553
95-th percentile41.65289033
Maximum45.77283113
Range28.14152058
Interquartile range (IQR)9.021982068

Descriptive statistics

Standard deviation6.026328075
Coefficient of variation (CV)0.193868013
Kurtosis-0.4479242398
Mean31.0846951
Median Absolute Deviation (MAD)4.032454177
Skewness0.5321358099
Sum2766.537864
Variance36.31663006
2020-07-24T18:06:11.013840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
28.7834670811.1%
 
32.8669868411.1%
 
37.1344139811.1%
 
29.5996733611.1%
 
28.4736224611.1%
 
26.4663034611.1%
 
25.1856609511.1%
 
27.3454245711.1%
 
25.9581748111.1%
 
25.5015663511.1%
 
Other values (79)7988.8%
 
ValueCountFrequency (%) 
17.6313105611.1%
 
22.6514813211.1%
 
23.3736198111.1%
 
23.427041511.1%
 
23.6448490911.1%
 
ValueCountFrequency (%) 
45.7728311311.1%
 
45.7189743611.1%
 
43.3386401611.1%
 
42.2229486811.1%
 
41.7689219111.1%
 

Interactions

2020-07-24T18:03:56.806943image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:57.274031image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:57.589829image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:58.108410image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:58.372927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:58.634946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:58.879990image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:59.481927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:59.725968image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:03:59.973383image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:00.295780image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:00.909029image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:01.174788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:01.447962image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:01.934166image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:02.473861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:02.721507image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:02.948881image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:03.180913image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:03.793860image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:04.021904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:04.283375image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:04.510926image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:04.977406image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:05.450418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:05.681932image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:05.936886image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:06.206953image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:06.867881image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:07.114780image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:07.411510image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:07.642283image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:08.306992image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:08.563777image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:08.797928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:09.032857image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:09.668133image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:09.963704image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:10.253477image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:10.529188image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:11.200919image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:11.475272image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:11.750872image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:12.017936image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:12.721027image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:13.035037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:13.361785image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:14.150813image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:14.443833image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:14.697869image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:14.996755image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:15.663812image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:15.944924image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:16.208936image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:16.463907image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:17.076802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:17.337253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:17.592827image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:17.796859image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:18.425275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:18.682900image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:18.901957image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:19.136103image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:19.369969image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:20.113176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:20.361806image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:20.646243image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:20.878858image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:21.565866image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:21.807966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-24T18:04:22.877696image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-24T18:04:24.787973image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:25.074950image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:25.323683image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:25.972870image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:26.267818image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:26.537349image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:26.965997image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:27.587193image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:27.900296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-24T18:04:28.973366image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-24T18:04:29.530153image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:29.982722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:30.508974image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:30.757908image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:31.023885image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:31.658172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:32.020243image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:32.303795image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:32.550860image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:33.268963image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:33.566162image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:33.844766image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-24T18:04:36.554829image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:36.806283image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:37.058650image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:37.686254image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:37.960809image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:38.194929image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:38.463729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:39.103620image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:39.344361image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:39.561385image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:39.789819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:40.034048image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:40.739036image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:40.992806image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:41.202849image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:41.451867image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:04:42.103647image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-24T18:06:12.395408image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-24T18:06:13.382655image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-24T18:06:14.194742image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-07-24T18:05:57.813877image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-24T18:05:59.171881image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

PROVINCIA Y CANTÓNJUNTASELECTORADOTOTAL GENERAL% PARTICIPACIÓNTOTALUNIDAD SOCIAL CRISTIANAINTEGRACIÓN NACIONALALIANZA PATRIÓTICARENOVACIÓN COSTARRICENSEFRENTE AMPLIOLIBERACIÓN NACIONALMOVIMIENTO LIBERTARIOACCIÓN CIUDADANAACCESIBILIDAD SIN EXCLUSIÓNNULOSBLANCOSABSOLUTO%
0COSTA RICA661728224911950847.069.117917191133374114.02049.03158.0139456782.0896516.0399788.0478877.036104.032555.06959.0871644.030.882083
1PROVINCIA SAN JOSÉ22321008177704284.069.85717869276524609.0970.0870.038522536.0322976.0122116.0200382.014454.09674.01845.0303893.030.142822
2SAN JOSÉ469226027153333.067.8383561510176436.0179.0164.0941618.070913.027690.040882.03194.02023.0293.072694.032.161644
3ESCAZÚ823985528967.072.68096928571688.021.031.011592.014627.05979.06427.0591.0345.051.010888.027.319031
4DESAMPARADOS30814363699995.069.616948982653711.0202.0123.0543337.045760.018539.026706.02344.01532.0198.043641.030.383052
5PURISCAL702426017626.072.65457517319640.011.036.06440.09127.03067.04086.0248.0213.094.06634.027.345425
6TARRAZÚ28103766954.067.0200466782351.017.014.03017.03263.01266.01737.087.0122.050.03422.032.979954
7ASERRÍ863690025385.068.79403824852945.078.024.0122113.011430.04977.06560.0603.0425.0108.011515.031.205962
8MORA431704312351.072.46963612053488.07.019.07837.05815.02551.02810.0248.0234.064.04692.027.530364
9GOICOECHEA1798570659764.069.731407588911945.0142.058.0401306.025171.010512.019077.01279.0768.0105.025942.030.268593

Last rows

PROVINCIA Y CANTÓNJUNTASELECTORADOTOTAL GENERAL% PARTICIPACIÓNTOTALUNIDAD SOCIAL CRISTIANAINTEGRACIÓN NACIONALALIANZA PATRIÓTICARENOVACIÓN COSTARRICENSEFRENTE AMPLIOLIBERACIÓN NACIONALMOVIMIENTO LIBERTARIOACCIÓN CIUDADANAACCESIBILIDAD SIN EXCLUSIÓNNULOSBLANCOSABSOLUTO%
79PARRITA3295036134.064.5480375942235.06.012.01129.03214.01849.0463.042.0153.039.03369.035.451963
80CORREDORES662713414714.054.22716914143463.08.040.018365.06257.03706.03260.0161.0479.092.012420.045.772831
81GARABITO2084575095.060.2459504976302.02.08.01412.02827.01248.0533.030.097.022.03362.039.754050
82PROVINCIA LIMÓN531224128137721.061.4474761327946640.0168.0345.02523682.055214.041733.023221.02268.04045.0882.086407.038.552524
83LIMÓN1426118035348.057.777051341791838.058.080.0520125.014045.09908.06889.0716.0932.0237.025832.042.222949
84POCOCÍ1707307247140.064.511714456301472.050.092.0820239.017254.016400.08475.0828.01291.0219.025932.035.488286
85SIQUIRRES843384120308.060.010047195701062.016.040.0379172.09717.05351.02511.0322.0631.0107.013533.039.989953
86TALAMANCA38150298900.059.2188448492988.014.023.012540.03473.01775.01998.056.0292.0116.06129.040.781156
87MATINA441799511559.064.23451011028806.014.069.045743.04851.03417.01266.0105.0414.0117.06436.035.765490
88GUÁCIMO532301114466.062.86558613895474.016.041.022263.05874.04882.02082.0241.0485.086.08545.037.134414